Atrial fibrillation is the most common sustained dysrhythmia worldwide. Over 2.3 million Americans are currently diagnosed, and the prevalence of AF is increasing with the aging of the U.S. population. Through its association with increased risk for heart failure, stroke and mortality, AF has a profound impact on the longevity and quality of life of a growing number of people. Although new AF treatment strategies have emerged over the last decade, a major challenge facing clinicians and researchers is the paroxysmal nature of AF, which makes it difficult to detect because it is short lasting, often asymptomatic, and intermittent. Thus, there is a pressing need to develop methods for accurate AF detection including paroxysmal rhythms. Such a method would have important clinical applications for pre- and post-treatment detection of AF. For these reasons, the importance of developing new AF detection technologies has been emphasized.
Many algorithms have been developed to detect AF and can be categorized as being based on 1) P-wave detection or 2) RR interval (RRI) variability. AF detection based on P-wave absence has not gained wide acceptance because determination of the P-wave fiducial point localization is challenging, especially for Holter monitoring applications. Indeed, for Holter monitoring, it is difficult to find uncontaminated RR intervals due to motion and noise artifacts which can confound the accuracy of P-wave detection. Subsequently, many studies have used variability of RR interval time series instead. Specifically, the aim is to quantify markedly increased beat-to-beat variability RR interval time series in AF. Consequently, most algorithms show higher sensitivity and specificity values than the methods that screen for the absence of P-waves. However, most of these RR intervals methods are based on comparing the density histogram of the data segment with previously-compiled standard density histograms of RR segments during AF using the Kolmogorov-Smirnov test. The main disadvantage of this approach is that it requires storage of large amounts of histogram data and threshold values of various characteristics of AF.
Accordingly, there is a need for improved methods and systems for detecting atrial fibrillation.